CaterpillarSSA 3.30
- License: Shareware
- Language: English, Russian
- Release: 2004-07-01
- Size: 837 Kb
- Cost: 99.50 $
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Awards
After collecting and analyzing of sent to us users' opinions about this program it takes our editors award with a rate "5". This means that this program is popular and was found as convenient and useful.
The program is based on the powerful model-free method of time series analysis Caterpillar (another name is SSA - Singular Spectrum Analysis). It combines advantages of other methods with simplicity of visual control aids. The basic Caterpillar-SSA algorithm for analyzing one-dimensional time series consists of transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique); Singular Value Decomposition of the trajectory matrix; reconstruction of the original time series based on a number of selected eigenvectors. The result of the Caterpillar-SSA processing is a natural decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. The method can be naturally extended to forecasting time series and its components, processing multidimensional time series and to change-point detection. The "Caterpillar" ideas were independently developed in Russia (St. Petersburg, Moscow) and also in UK and USA (under the name of SSA; that is, Singular Spectrum Analysis). The new book "Analysis of Time Series Structure: SSA and Related Techniques", authors are N. Golyandina, V. Nekrutkin and A. Zhigljavsky, provides a careful, lucid description of SSA general theory and methodology (in English, Chapman&Hall/CRC, see http://www.gistatgroup.com/cat/). The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and nonstationary, almost deterministic and noisy time series are to be analyzed. We are sure, that in a near future "Caterpillar"-like methods will rank among the base methods of time series analysis and will be included in standard statistical software.
Keywords: spectrum value principal statistics time decomposition reconstruction periodicities analysis series forecast seasonalities software component singular approximation trend
Latest User Reviews:
Sbeau, 2008-08-11 17:20:23
Version: 3.30
To tell you the truth, I only spent a few minutes looking at your software. I frankly could not see what it was doing with the sample data. I also only glanced at the reference articles on the technique and it was not immediately clear what CaterpillarSSA was giving me.
Just so you know, I have graduate-level training in Machine Learning and am familiar with various spectral data analysis techniques. I'm looking for techniques that might be used for a estimating the frequencies of a finite number (10-50) of components in a signal. Some of the signals might be closely spaced. Imagine a kind of smart Goertzel algorithm ...
The best candidate algorithm I've found so far is Matching Pursuit (by Chen and Donoho) with an overcomplete sinusoidal dictionary.
Can CaterpillarSSA solve my problem?
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